{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Handling uncertainty with quantile regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-25T21:18:46.804020Z",
     "iopub.status.busy": "2020-11-25T21:18:46.803558Z",
     "iopub.status.idle": "2020-11-25T21:18:47.039577Z",
     "shell.execute_reply": "2020-11-25T21:18:47.039016Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Quantile regression](https://www.wikiwand.com/en/Quantile_regression) is useful when you're not so much interested in the accuracy of your model, but rather you want your model to be good at ranking observations correctly. The typical way to perform quantile regression is to use a special loss function, namely the quantile loss. The quantile loss takes a parameter, $\\alpha$ (alpha), which indicates which quantile the model should be targeting. In the case of $\\alpha = 0.5$, then this is equivalent to asking the model to predict the median value of the target, and not the most likely value which would be the mean. \n",
    "\n",
    "A nice thing we can do with quantile regression is to produce a prediction interval for each prediction. Indeed, if we predict the lower and upper quantiles of the target then we will be able to obtain a \"trust region\" in between which the true value is likely to belong. Of course, the likeliness will depend on the chosen quantiles. For a slightly more detailed explanation see [this](https://medium.com/the-artificial-impostor/quantile-regression-part-1-e25bdd8d9d43) blog post.\n",
    "\n",
    "As an example, let us take the [simple time series model we built in another notebook](building-a-simple-time-series-model.md). Instead of predicting the mean value of the target distribution, we will predict the 5th, 50th, 95th quantiles. This will require training three separate models, so we will encapsulate the model building logic in a function called `make_model`. We also have to slightly adapt the training loop, but not by much. Finally, we will draw the prediction interval along with the predictions from for 50th quantile (i.e. the median) and the true values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-25T21:18:47.075256Z",
     "iopub.status.busy": "2020-11-25T21:18:47.074021Z",
     "iopub.status.idle": "2020-11-25T21:18:48.006555Z",
     "shell.execute_reply": "2020-11-25T21:18:48.006940Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'MAE: 15.634343')"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import calendar\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "from river import compose\n",
    "from river import datasets\n",
    "from river import linear_model\n",
    "from river import metrics\n",
    "from river import optim\n",
    "from river import preprocessing\n",
    "from river import stats\n",
    "from river import time_series\n",
    "    \n",
    "\n",
    "def get_ordinal_date(x):\n",
    "    return {'ordinal_date': x['month'].toordinal()}    \n",
    "\n",
    "    \n",
    "def get_month_distances(x):\n",
    "    return {\n",
    "        calendar.month_name[month]: math.exp(-(x['month'].month - month) ** 2)\n",
    "        for month in range(1, 13)\n",
    "    }\n",
    "        \n",
    "\n",
    "def make_model(alpha):\n",
    "    \n",
    "    extract_features = compose.TransformerUnion(get_ordinal_date, get_month_distances)\n",
    "\n",
    "    scale = preprocessing.StandardScaler()\n",
    "\n",
    "    learn = linear_model.LinearRegression(\n",
    "        intercept_lr=0,\n",
    "        optimizer=optim.SGD(3),\n",
    "        loss=optim.losses.Quantile(alpha=alpha)\n",
    "    )\n",
    "\n",
    "    model = extract_features | scale | learn\n",
    "    model = time_series.Detrender(regressor=model, window_size=12)\n",
    "\n",
    "    return model\n",
    "\n",
    "metric = metrics.MAE()\n",
    "\n",
    "models = {\n",
    "    'lower': make_model(alpha=0.05),\n",
    "    'center': make_model(alpha=0.5),\n",
    "    'upper': make_model(alpha=0.95)\n",
    "}\n",
    "\n",
    "dates = []\n",
    "y_trues = []\n",
    "y_preds = {\n",
    "    'lower': [],\n",
    "    'center': [],\n",
    "    'upper': []\n",
    "}\n",
    "\n",
    "for x, y in datasets.AirlinePassengers():\n",
    "    y_trues.append(y)\n",
    "    dates.append(x['month'])\n",
    "    \n",
    "    for name, model in models.items():\n",
    "        y_preds[name].append(model.predict_one(x))\n",
    "        model.learn_one(x, y)\n",
    "\n",
    "    # Update the error metric\n",
    "    metric.update(y, y_preds['center'][-1])\n",
    "\n",
    "# Plot the results\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "ax.grid(alpha=0.75)\n",
    "ax.plot(dates, y_trues, lw=3, color='#2ecc71', alpha=0.8, label='Truth')\n",
    "ax.plot(dates, y_preds['center'], lw=3, color='#e74c3c', alpha=0.8, label='Prediction')\n",
    "ax.fill_between(dates, y_preds['lower'], y_preds['upper'], color='#e74c3c', alpha=0.3, label='Prediction interval')\n",
    "ax.legend()\n",
    "ax.set_title(metric);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An important thing to note is that the prediction interval we obtained should not be confused with a confidence interval. Simply put, a prediction interval represents uncertainty for where the true value lies, whereas a confidence interval encapsulates the uncertainty on the prediction. You can find out more by reading [this](https://stats.stackexchange.com/questions/16493/difference-between-confidence-intervals-and-prediction-intervals) CrossValidated post."
   ]
  }
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